AI-Supported Inclusive Technologies in Higher Education Automatic Adaptation of Interfaces and Learning Materials for Students with Disabilities

Abstract

Digital learning platforms often fail students with disabilities who need responsive, personalized interfaces and materials. This research examines how artificial intelligence automatically adapts interfaces and learning materials within higher education. It aims to determine how such adaptation affects academic outcomes for disabled students. Using a qualitative doctrinal design, the study analyzes legal instruments, institutional policies, and scholarly literature through document analysis. Findings show that interface adaptation improved accessibility for visually impaired students, while material adaptation strengthened comprehension for cognitively disabled learners. Predictive personalization reduced dependence on manual accommodation requests, and combined adaptation produced stronger engagement than isolated features alone. Faculty training emerged as essential for effective implementation across institutions. The research recommends pairing adaptive technology with structured staff training and updated institutional accessibility policies. It concludes that automatic adaptation represents meaningful progress toward inclusive higher education, though broader investigation remains necessary to confirm these findings across diverse institutional contexts worldwide.